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. 2015 Feb;73(2):555-64.
doi: 10.1002/mrm.25158. Epub 2014 Mar 6.

Three-dimensional pulmonary perfusion MRI with radial ultrashort echo time and spatial-temporal constrained reconstruction

Grzegorz Bauman  1 , Kevin M Johnson, Laura C Bell, Julia V Velikina, Alexey A Samsonov, Scott K Nagle, Sean B Fain
Affiliations

Three-dimensional pulmonary perfusion MRI with radial ultrashort echo time and spatial-temporal constrained reconstruction

Grzegorz Bauman et al. Magn Reson Med. 2015 Feb.

Abstract

Purpose: To assess the feasibility of spatial-temporal constrained reconstruction for accelerated regional lung perfusion using highly undersampled dynamic contrast-enhanced (DCE) three-dimensional (3D) radial MRI with ultrashort echo time (UTE).

Methods: A combined strategy was used to accelerate DCE MRI for 3D pulmonary perfusion with whole lung coverage. A highly undersampled 3D radial UTE MRI acquisition was combined with an iterative constrained reconstruction exploiting principal component analysis and wavelet soft-thresholding for dimensionality reduction in space and time. The performance of the method was evaluated using a 3D fractal-based DCE digital lung phantom. Simulated perfusion maps and contrast enhancement curves were compared with ground truth using the structural similarity index (SSIM) to determine robust threshold and regularization levels. Feasibility studies were then performed in a canine and a human subject with 3D radial UTE (TE=0.08 ms) acquisition to assess feasibility of mapping regional 3D perfusion.

Results: The method was able to accurately recover perfusion maps in the phantom with a nominal isotropic spatial resolution of 1.5 mm (SSIM of 0.949). The canine and human subject studies demonstrated feasibility for providing artifact-free perfusion maps in a simple 3D breath-held acquisition.

Conclusion: The proposed method is promising for fast and flexible 3D pulmonary perfusion imaging. Magn Reson

Keywords: MRI; UTE; compressed sensing; image reconstruction; lung perfusion; principal component analysis; radial; wavelets.

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Figures

Figure 1
Figure 1
Visualization of the arterial (a), parenchymal (b) and venous (c) compartments of the fractal based digital lung phantom.
Figure 2
Figure 2
Workflow of the principal component basis with wavelet soft-thresholding (PCB+ST) reconstruction algorithm. Raw data is used to calculate the time-resolved training data at low spatial resolution using wavelet regularization. The principal component analysis (PCA) was performed on the reconstructed training data set. Several principal components corresponding to largest singular values were chosen as a new temporal basis. Subsequently, the high-resolution time-resolved data set was iteratively reconstructed using the PCA constraint and/or wavelet soft-thresholding depending on the reconstruction tested.
Figure 3
Figure 3
Comparison between the fractal-based 3D digital lung phantom ground truth and the different reconstruction techniques. All images show identical slice orientation and time frame (t=23 s). The arrows indicate pulmonary artery (Pa), pulmonary vein (Pv) and a wedge-shaped region with decreased signal intensity located in peripheral part of the upper left lung (Pd).
Figure 4
Figure 4
Contrast-enhancement curves measured in regions of interest located in pulmonary artery (a), pulmonary vein (b), lung parenchyma (c), wedge-shaped perfusion defect (d) for ground truth lung phantom and the time-resolved series reconstructed using different techniques. Mean squared error (MSE) calculated between the reference curves and curves obtained from image reconstructions. The largest MSE was for PILS-VS: 0.1823 (arterial curve), 0.0218 (venous curve), 0.0288 (parenchymal curve), 0.0118 (perfusion defect) and the smallest MSE was for PCB+ST: 0.0055 (arterial curve), 0.0131 (venous curve), 0.0105 (parenchymal curve), 0.0053 (perfusion defect).
Figure 5
Figure 5
Structural similarity index (SSIM) between the fractal-based ground truth lung phantom and time-resolved data sets reconstructed using FISTA, PCB and PCB+ST. PILS-VS and PILS results are not shown for clarity. For comparison, SSIM was 0.752 for PILS-VS and for 0.459 PILS.
Figure 6
Figure 6
Numerical evaluation of simulated PBF (ml blood / 100ml lung / min) (a), PBV (ml blood / 100ml lung) (b) and MTT (s) (c) in a coronal slice in Figure 3. The values in the lower left side in each image indicate the mean and standard deviation calculated from the region of interest in the whole right lung excluding vessels. The ROI used for the quantitative results in Table 1 is indicated in the upper left panel.
Figure 7
Figure 7
A transverse slice representing a single time frame obtained from the DCE UTE acquisition in canine subject reconstructed using PILS, PILS with view sharing (VS), FISTA, PCB and PCB+ST.
Figure 8
Figure 8
Quantitative evaluation of PBF (ml blood / 100ml lung / min), PBV (ml blood / 100ml lung) and MTT (s) showing a transverse slice obtained from the DCE UTE scan in the dog study with parametric color maps overlain on the morphological images. Below contrast-enhancement curves measured in regions of interest located in pulmonary artery, pulmonary vein and lung parenchyma in canine subject for the time-resolved series reconstructed using different techniques.
Figure 9
Figure 9
Quantitative evaluation of PBF (ml blood / 100ml lung / min), PBV (ml blood / 100ml lung) and MTT (s) in a 23 years old female patient with cardiomyopathy using the proposed PCB+ST method.
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